Abstract: Objective The Junggar Basin is an important oil- and gas-bearing basin in China, and the exploration targets have extended into deeper layers. The complex near-surface conditions, exploration target depth, and new 3D seismic data acquisition methods (the wide-azimuth, wide-bandwidth, and high-density data acquisition) in the basin have led to some challenges, such as low signal-to-noise ratio (SNR) and large data volumes, which affect the finding of exploration goals. Therefore, suppressing noise and improving the quality of 3D seismic data within the basin is crucial for finding the exploration goals. Method In recent years, the rapid development of deep learning theory has resulted in the great learning capabilities of deep neural networks, while the significant enhancement of hardware performance has enabled higher processing efficiency. Based on residual learning and batch normalization techniques, this study developed a 3D denoising convolutional neural network (3D-DnCNN) and proposed a deep learning-based 3D seismic data noise suppression workflow suited to the Junggar Basin. Results and Conclusion To suppress the noise for the dataset from a large area in the Junggar Basin, this paper selected noise suppression results from high-coverage, high-SNR areas to construct high-quality labels and applied the trained 3D-DnCNN network to the entire area. Compared with the results of conventional industrial workflows, the results of this study show better event consistency, intact fault preservation, and clearer top boundaries and internal features of the Carboniferous strata. Moreover, since the 3D-DnCNN network learned the characteristics of migration arch noise in high-SNR areas, its ability to suppress such noise across the entire area was also significantly superior to conventional industrial workflows. By adjusting network parameters (such as network depth, convolution kernel size, and training sample selection strategy), the network can be further optimized to adapt to seismic data from different regions, thereby enhancing the applicability and effectiveness of seismic noise suppression techniques.